Final Multiqc Report

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.25.2

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        Final Multiqc Report

        https://github.com/Daylily-Informatics/daylily (BRANCH:* sentb) (TAG:sentbgo2) (HASH:7152197a)
        Project/Budget
        da-us-west-2d-lsmc-senttests-usw2d
        Budget @ Runtime
        $93.546 of $200.0 spent ( 46.773%)
        Spot Instances
        c6i.32xlarge c6i.metal c7i.48xlarge c7i.metal-48xl m6i.32xlarge m6i.metal m7i.48xlarge m7i.metal-48xl r6i.metal r7i.48xlarge
        Spot Costs per hr
        median: $2.86 mean: $3.00 ( avg cost per vcpu,per min: $0.000260682 )
        FQ->BAM.sort avg Costs
        sent.alNsort: $0.14, bwa2a.alNsort: $0.04, strobe.alNsort: $0.02
        BAM mrkdup avg Cost
        0.21 min, costing $0.01
        Results Dir (GB)
        2.1G

        Report generated on 2025-05-02, 08:33 UTC based on data in:


        General Statistics

        Showing 0/29 rows and 37/68 columns.
        Sample NameContamination (S)DupsGCAvg lenMedian lenFailedSeqsVarsSNPIndelTs/TvMNPMultiallelicMultiallelic SNPError rateNon-primaryReads mapped% Mapped% Proper pairs% MapQ 0 readsTotal seqsMean insertReadsReads mapped% Reads mapped% GCIns. size≥ 1X≥ 5X≥ 10X≥ 30X≥ 50XMedian covMean covError rate% AlignedM AlignedM Total reads≥ 1X≥ 5X≥ 10X≥ 30X≥ 50XMedianMean Cov.Min Cov.Max Cov.Mb Total Coverage BasesGenome lengthDupsGCAvg lenMedian lenFailedSeqsVarsSNPIndelTs/TvMNPMultiallelicMultiallelic SNPInsertSizeMeanInsertSizeMedianInsertSizeModeInsertSizeStandardDeviationDuplicateReadsPctChimericReadPairPct
        RIH0_ANA0-HG002_DBC0_0
        40%
        563
        1.1%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        RIH0_ANA0-HG002_DBC0_0.R1
        0.2%
        39.0%
        148bp
        148bp
        0%
        0.1M
        0.2%
        39.0%
        148bp
        148bp
        0%
        0.1M
        RIH0_ANA0-HG002_DBC0_0.R2
        0.2%
        39.0%
        148bp
        148bp
        0%
        0.1M
        0.2%
        39.0%
        148bp
        148bp
        0%
        0.1M
        RIH0_ANA0-HG002_DBC0_0.strobe
        0.000%
        0.62%
        0.1M
        0.2M
        99.1%
        95.9%
        3.9%
        0.2M
        648.8bp
        0.3M
        0.3M
        99.2%
        40%
        563
        1.1%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        288.0X
        35.7Mb
        3,088,286,401
        562.0
        564.0
        546.0
        162.1
        0.0
        2.9
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr1
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        51.0X
        3.0Mb
        248,956,422
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr2
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        13.0X
        2.9Mb
        242,193,529
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr3
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        288.0X
        2.5Mb
        198,295,559
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr4
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        65.0X
        2.4Mb
        190,214,555
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr5
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        27.0X
        2.2Mb
        181,538,259
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr6
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        4.0X
        2.1Mb
        170,805,979
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr7
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        3.0X
        1.9Mb
        159,345,973
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr8
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        7.0X
        1.7Mb
        145,138,636
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr9
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        11.0X
        1.4Mb
        138,394,717
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr10
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        9.0X
        1.6Mb
        133,797,422
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr11
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        3.0X
        1.6Mb
        135,086,622
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr12
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        3.0X
        1.6Mb
        133,275,309
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr13
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        14.0X
        1.3Mb
        114,364,328
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr14
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        3.0X
        1.1Mb
        107,043,718
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr15
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        3.0X
        1.0Mb
        101,991,189
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr16
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        30.0X
        1.0Mb
        90,338,345
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr17
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        16.0X
        0.9Mb
        83,257,441
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr18
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        8.0X
        0.9Mb
        80,373,285
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr19
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        3.0X
        0.6Mb
        58,617,616
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr20
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        25.0X
        0.7Mb
        64,444,167
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr21
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        40.0X
        0.5Mb
        46,709,983
        RIH0_ANA0-HG002_DBC0_0.strobe.md.chr22
        1.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0X
        0.0X
        0.0X
        9.0X
        0.4Mb
        50,818,468
        RIH0_ANA0-HG002_DBC0_0.strobe.mrkdup.sort
        0.0X
        0.62%
        99.1%
        0.2M
        0.2M
        RIH0_ANA0-HG002_DBC0_0.strobe.sentd.snv.sort
        221
        116
        105
        0.92
        0
        3
        1
        221
        116
        105
        0.92
        0
        3
        1
        RIH0_ANA0-HG002_DBC0_0.strobe.st
        0.62%
        0.1M
        0.2M
        99.1%
        95.9%
        3.9%
        0.2M
        648.8bp
        0.3M
        0.3M
        99.2%

        VerifyBAMID

        Detects sample contamination and/or sample swaps.URL: https://genome.sph.umich.edu/wiki/VerifyBamIDDOI: 10.1016/j.ajhg.2012.09.004

        VerifyBamID checks whether reads match known genotypes or are contaminated as a mixture of two samples. A key step in any genetic analysis is to verify whether data being generated matches expectations. verifyBamID checks whether reads in a BAM file match previous genotypes for a specific sample. In addition, it detects possible sample mixture from population allele frequency only, which can be particularly useful when the genotype data is not available. Using a mathematical model that relates observed sequence reads to an hypothetical true genotype, verifyBamID tries to decide whether sequence reads match a particular individual or are more likely to be contaminated (including a small proportion of foreign DNA), derived from a closely related individual, or derived from a completely different individual.

        The following values provide estimates of sample contamination. Click help for more information.

        Please note that FREEMIX is named Contamination (Seq) and CHIPMIX is named Contamination (S+A) in this MultiQC report.

        VerifyBamID provides a series of information that is informative to determine whether the sample is possibly contaminated or swapped, but there is no single criteria that works for every circumstances. There are a few unmodeled factor in the estimation of [SELF-IBD]/[BEST-IBD] and [%MIX], so please note that the MLE estimation may not always exactly match to the true amount of contamination. Here we provide a guideline to flag potentially contaminated/swapped samples:

        • Each sample or lane can be checked in this way. When [CHIPMIX] >> 0.02 and/or [FREEMIX] >> 0.02, meaning 2% or more of non-reference bases are observed in reference sites, we recommend to examine the data more carefully for the possibility of contamination.
        • We recommend to check each lane for the possibility of sample swaps. When [CHIPMIX] ~ 1 AND [FREEMIX] ~ 0, then it is possible that the sample is swapped with another sample. When [CHIPMIX] ~ 0 in .bestSM file, [CHIP_ID] might be actually the swapped sample. Otherwise, the swapped sample may not exist in the genotype data you have compared.
        • When genotype data is not available but allele-frequency-based estimates of [FREEMIX] >= 0.03 and [FREELK1]-[FREELK0] is large, then it is possible that the sample is contaminated with other sample. We recommend to use per-sample data rather than per-lane data for checking this for low coverage data, because the inference will be more confident when there are large number of bases with depth 2 or higher.

        Copied from the VerifyBAMID documentation - see the link for more details.

        Showing 0/1 rows and 7/12 columns.
        Sample NameRead GroupSNPSM ReadsAverage DepthContamination (Seq)FREEELK1FREELK0FREE_RHFREE_RADPREFRDPHETRDPALT
        RIH0_ANA0-HG002_DBC0_0.strobe
        NA
        NA
        NAM
        NAX
        0.000%
        -1
        -1
        NA
        NA
        NA
        NA
        NA

        Samtools

        Version: 1.21

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat

        Created with MultiQC

        XY counts

        Created with MultiQC

        Mapped reads per contig

        The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

        Created with MultiQC

        QualiMap

        Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

        Coverage histogram

        Distribution of the number of locations in the reference genome with a given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

        Created with MultiQC

        Cumulative genome coverage

        Percentage of the reference genome with at least the given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Insert size histogram

        Distribution of estimated insert sizes of mapped reads.

        To overcome limitations in the length of DNA or RNA sequencing reads, many sequencing instruments can produce two or more shorter reads from one longer fragment in which the relative position of reads is approximately known, such as paired-end or mate-pair reads (Mardis 2013). Such techniques can extend the reach of sequencing technology, allowing for more accurate placement of reads (Reinert et al. 2015) and better resolution of repeat regions (Reinert et al. 2015), as well as detection of structural variation (Alkan et al. 2011) and chimeric transcripts (Maher et al. 2009).

        All these methods assume that the approximate size of an insert is known. (Insert size can be defined as the length in bases of a sequenced DNA or RNA fragment, excluding technical sequences such as adapters, which are typically removed before alignment.) This plot allows for that assumption to be assessed. With the set of mapped fragments for a given sample, QualiMap groups the fragments by insert size, then plots the frequency of mapped fragments (y-axis) over a range of insert sizes (x-axis). In an ideal case, the distribution of fragment sizes for a sequencing library would culminate in a single peak indicating average insert size, with a narrow spread indicating highly consistent fragment lengths.

        QualiMap calculates insert sizes as follows: for each fragment in which every read mapped successfully to the same reference sequence, it extracts the insert size from the TLEN field of the leftmost read (see the Qualimap 2 documentation), where the TLEN (or 'observed Template LENgth') field contains 'the number of bases from the leftmost mapped base to the rightmost mapped base' (SAM format specification). Note that because it is defined in terms of alignment to a reference sequence, the value of the TLEN field may differ from the insert size due to factors such as alignment clipping, alignment errors, or structural variation or splicing in a gap between reads from the same fragment.

        Created with MultiQC

        GC content distribution

        Each solid line represents the distribution of GC content of mapped reads for a given sample.

        GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).

        QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).

        Created with MultiQC

        Mosdepth

        Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.URL: https://github.com/brentp/mosdepthDOI: 10.1093/bioinformatics/btx699

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 23 samples, a BED file was provided, so the data was calculated across those regions. For 23 samples, it's calculated across the entire genome length. 23 samples have both global and region reports, and we are showing the data for regions

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        XY coverage

        Created with MultiQC

        goleft indexcov

        Quickly estimate coverage from a whole-genome bam index, providing 16KB resolution.URL: https://github.com/brentp/goleft/tree/master/indexcovDOI: 10.1093/gigascience/gix090

        This is useful as a quick QC to get coverage values across the genome.

        Scaled coverage ROC plot

        Coverage (ROC) plot that shows genome coverage at given (scaled) depth.

        Lower coverage samples have shorter curves where the proportion of regions covered drops off more quickly. This indicates a higher fraction of low coverage regions.

        Created with MultiQC

        Problem coverage bins

        This plot identifies problematic samples using binned coverage distributions.

        We expect bins to be around 1, so deviations from this indicate problems. Low coverage bins (< 0.15) on the x-axis have regions with low or missing coverage. Higher values indicate truncated BAM files or missing data. Bins with skewed distributions (<0.85 or >1.15) on the y-axis detect dosage bias. Large values on the y-axis are likely to impact CNV and structural variant calling. See the goleft indexcov bin documentation for more details.

        Created with MultiQC

        FastQC

        Version: 0.11.9

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (148bp)

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        2 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/0 rows.
        Overrepresented sequence

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Bcftools

        Version: 1.21

        Utilities for variant calling and manipulating VCFs and BCFs.URL: https://samtools.github.io/bcftoolsDOI: 10.1093/gigascience/giab008

        Variant Substitution Types

        Created with MultiQC

        Variant Quality

        Created with MultiQC

        Indel Distribution

        Created with MultiQC

        Variant depths

        Read depth support distribution for called variants

        Created with MultiQC

        Coverage Eveness Metrics

        Coverage Eveness Metrics.URL: https://en.wikipedia.org/wiki/Margaret_Oakley_Dayhoff

        Showing 0/1 rows and 13/13 columns.
        SampleCHRMmeanRawCovmedianRawCovstdevRawCovRawCovCoefofvarNCmeanNCmedianstdevNCNCcoefofvarpctEQ0pctLT5pctLT10aligner
        RIH0_ANA0-HG002_DBC0_0
        chr22
        0.0
        0.0
        0.1
        11.6
        nan
        NA
        NA
        NA
        0.0
        0.0
        0.0
        strobe

        Controls Concordance Report

        Concordance Stats For The GIAB HC Regions For This Sample.

        Showing 0/9 rows and 19/19 columns.
        mqc_idSNPClassSampleTgtRegionSizeTNFNTPFPFscoreSensitivity-RecallSpecificityFDRPPVPrecisionAltIdCmpFootprintAllVarMeanDPCovBinAlignerSNVCaller
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-All
        All
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,805,318,548.0
        3,873,651.0
        51.0
        37.0
        0.0
        0.0
        1.0
        0.4
        0.6
        0.6
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-DEL_50
        DEL_50
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,808,990,307.0
        201,924.0
        19.0
        2.0
        0.0
        0.0
        1.0
        0.1
        0.9
        0.9
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-DEL_gt50
        DEL_gt50
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,809,192,219.0
        31.0
        0.0
        0.0
        0.0
        1.0
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-INS_50
        INS_50
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,808,957,767.0
        234,467.0
        16.0
        7.0
        0.0
        0.0
        1.0
        0.3
        0.7
        0.7
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-INS_gt50
        INS_gt50
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,809,192,183.0
        67.0
        0.0
        0.0
        0.0
        1.0
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-Indel_50
        Indel_50
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,809,166,709.0
        25,541.0
        0.0
        0.0
        0.0
        1.0
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-Indel_gt50
        Indel_gt50
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,809,191,249.0
        1,001.0
        0.0
        0.0
        0.0
        1.0
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-SNPts
        SNPts
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,806,878,564.0
        2,313,672.0
        14.0
        18.0
        0.0
        0.0
        1.0
        0.6
        0.4
        0.4
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd
        RIH0_ANA0-HG002_DBC0_0-strobe-sentd-SNPtv
        SNPtv
        RIH0_ANA0-HG002_DBC0_0
        2,809,192,250.0
        2,808,095,300.0
        1,096,948.0
        2.0
        10.0
        0.0
        0.0
        1.0
        0.8
        0.2
        0.2
        HG002
        ultima
        -1.0
        -2.0
        strobe
        sentd

        Task Benchmark Performances

        Workflow Task Benchmark Results.

        Showing 0/29 rows and 23/23 columns.
        combined_rulesamplerulesh:m:smax_rssmax_vmsmax_ussmax_pssio_inio_outmean_loadcpu_timehostnameipnproccpu_efficiencyinstance_typeregion_azspot_costsnakemake_threadstask_costrule_prefixrule_suffix
        alignstats_smmary_compile-all.
        all.
        alignstats_smmary_compile
        0.7
        0:00:00
        15.1
        31.9
        8.2
        8.8
        3.9
        0.0
        0.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.1
        r7i.2xlarge
        us-west-2d
        0.2
        2.0
        0.0
        alignstats_smmary_compile
        NA
        alignstats_summary-all.
        all.
        alignstats_summary
        0.3
        0:00:00
        3.2
        7.7
        0.1
        0.4
        0.0
        0.0
        0.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        1.0
        0.0
        alignstats_summary
        NA
        bwa2a.alNsort-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        bwa2a.alNsort
        45.6
        0:00:45
        16,352.0
        19,244.8
        16,337.6
        16,338.8
        0.0
        0.0
        36.0
        16.4
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.4
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        bwa2a
        alNsort
        bwa2a.mrkdup-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        bwa2a.mrkdup
        6.7
        0:00:06
        23,588.3
        26,433.4
        23,314.2
        23,314.3
        0.0
        11.3
        183.8
        12.5
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.9
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        bwa2a
        mrkdup
        bwa2a.vb2-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        bwa2a.vb2
        44.8
        0:00:44
        1,410.4
        79,307.9
        1,284.0
        1,295.7
        135.6
        8.4
        120.8
        9.7
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.2
        r7i.2xlarge
        us-west-2d
        0.2
        4.0
        0.0
        bwa2a
        vb2
        dirsetup-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        dirsetup
        0.3
        0:00:00
        16.4
        39.0
        7.2
        7.8
        0.0
        0.0
        0.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        1.0
        0.0
        dirsetup
        NA
        fastqc-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        fastqc
        5.1
        0:00:05
        326.9
        4,842.4
        321.7
        322.0
        99.6
        3.7
        137.9
        7.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.4
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        fastqc
        NA
        final_multiqc-DAY_all.
        DAY_all.
        final_multiqc
        6.6
        0:00:06
        146.0
        2,773.8
        141.0
        142.8
        907.3
        3.6
        55.4
        3.8
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.6
        r7i.2xlarge
        us-west-2d
        0.2
        4.0
        0.0
        final_multiqc
        NA
        sent.alNsort-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        sent.alNsort
        169.9
        0:02:49
        19,802.6
        28,772.7
        19,769.8
        19,777.5
        0.0
        0.0
        578.1
        982.2
        ip-10-0-0-110
        44.238.210.144
        8.0
        5.8
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        sent
        alNsort
        sent.mrkdup-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        sent.mrkdup
        24.1
        0:00:24
        14,838.8
        26,165.4
        14,834.3
        14,834.5
        26.1
        0.0
        422.8
        102.2
        ip-10-0-0-110
        44.238.210.144
        8.0
        4.2
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        sent
        mrkdup
        sent.vb2-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        sent.vb2
        44.8
        0:00:44
        1,302.9
        79,307.9
        1,178.6
        1,189.2
        117.7
        8.4
        116.0
        8.8
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.2
        r7i.2xlarge
        us-west-2d
        0.2
        4.0
        0.0
        sent
        vb2
        strobe.alNsort-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.alNsort
        21.2
        0:00:21
        13,981.0
        14,067.6
        13,966.0
        13,967.3
        11,264.2
        0.0
        68.3
        14.6
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.7
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        alNsort
        strobe.alignstats-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.alignstats
        7.9
        0:00:07
        968.4
        1,555.9
        964.4
        964.6
        1.6
        0.0
        92.3
        7.5
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.9
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        alignstats
        strobe.goleft-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.goleft
        2.0
        0:00:02
        29.5
        125.0
        27.4
        27.5
        19.9
        6.7
        81.5
        1.8
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.9
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        goleft
        strobe.mosdepth-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.mosdepth
        56.7
        0:00:56
        14,429.5
        19,132.2
        14,425.5
        14,425.8
        110.0
        0.2
        77.6
        44.5
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.8
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        mosdepth
        strobe.mrkdup-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.mrkdup
        6.6
        0:00:06
        23,768.4
        26,500.3
        23,413.8
        23,413.9
        0.0
        14.0
        185.9
        12.5
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.9
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        mrkdup
        strobe.mrkdup.sort.picard-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.mrkdup.sort.picard
        65.1
        0:01:05
        1,047.2
        19,132.2
        1,036.5
        1,037.6
        913.6
        0.9
        65.8
        43.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.7
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        mrkdup.sort.picard
        strobe.norm_cov_eveness-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.norm_cov_eveness
        418.0
        0:06:57
        982.4
        19,132.2
        978.0
        978.3
        493.7
        1,947.0
        3.2
        2.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        norm_cov_eveness
        strobe.qmap-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.qmap
        14.7
        0:00:14
        1,841.6
        54,789.9
        1,832.5
        1,833.0
        196.6
        0.7
        136.8
        20.2
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.4
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        qmap
        strobe.samt-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.samt
        1.3
        0:00:01
        16.0
        412.7
        6.8
        7.4
        36.4
        0.0
        76.2
        1.2
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.0
        r7i.2xlarge
        us-west-2d
        0.2
        8.0
        0.0
        strobe
        samt
        strobe.sentd.19-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.19
        9.3
        0:00:09
        397.0
        817.2
        379.9
        384.9
        99.8
        0.4
        137.5
        10.6
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.1
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        sentd.19
        strobe.sentd.21-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.21
        8.0
        0:00:08
        396.6
        872.4
        379.9
        385.0
        46.1
        0.4
        156.6
        10.8
        ip-10-0-0-110
        44.238.210.144
        8.0
        1.3
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        sentd.21
        strobe.sentd.bcfstat-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.bcfstat
        0.4
        0:00:00
        3.4
        7.7
        0.1
        0.3
        0.0
        0.0
        0.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        sentd.bcfstat
        strobe.sentd.concat.fofn-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.concat.fofn
        0.4
        0:00:00
        3.4
        7.7
        0.1
        0.3
        0.0
        0.0
        0.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        1.0
        0.0
        strobe
        sentd.concat.fofn
        strobe.sentd.concordance-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.concordance
        1,047.3
        0:17:27
        17,108.6
        63,202.8
        17,097.5
        17,098.0
        3,063.3
        4,613.1
        12.7
        0.7
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.1
        strobe
        sentd.concordance
        strobe.sentd.merge-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.merge
        0.5
        0:00:00
        3.4
        7.7
        0.1
        0.3
        0.0
        0.0
        0.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        4.0
        0.0
        strobe
        sentd.merge
        strobe.sentd.peddy-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.peddy
        7.7
        0:00:07
        134.3
        1,404.9
        127.0
        127.8
        133.6
        0.1
        35.0
        0.0
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.0
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        sentd.peddy
        strobe.sentd.rtgvcfstats-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.sentd.rtgvcfstats
        0.8
        0:00:00
        46.6
        2,812.7
        30.9
        31.2
        0.0
        0.1
        0.0
        0.1
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.1
        r7i.2xlarge
        us-west-2d
        0.2
        7.0
        0.0
        strobe
        sentd.rtgvcfstats
        strobe.vb2-RIH0_ANA0-HG002
        RIH0_ANA0-HG002
        strobe.vb2
        21.9
        0:00:21
        1,371.0
        79,307.9
        1,259.0
        1,279.9
        239.7
        15.6
        394.2
        11.5
        ip-10-0-0-110
        44.238.210.144
        8.0
        0.5
        r7i.2xlarge
        us-west-2d
        0.2
        4.0
        0.0
        strobe
        vb2

        Alignstats Data

        Alignstats Data.

        Showing 0/1 rows and 166/166 columns.
        samplealignerAlignedBasesAlignedBasesPctAlignedReadLengthMeanAlignedReadLengthMedianAlignedReadLengthModeAlignedReadLengthStandardDeviationChimericReadPairPctDeletedBasesDeletedBasesPctDuplicateBasesDuplicateBasesPctDuplicateReadsDuplicateReadsPctFilteredRecordsFilteredRecordsPctInputFileNameInputFileSizeInsertSizeMeanInsertSizeMedianInsertSizeModeInsertSizeStandardDeviationInsertedBasesInsertedBasesPctMappedBasesMappedBasesPctMappedReadsMappedReadsPctMatchedBasesMatchedBasesPctMismatchedBasesMismatchedBasesPctPerfectBasesPerfectBasesPctPerfectReadsPerfectReadsPctQ20BasesQ20BasesPctR1AlignedBasesR1AlignedBasesPctR1AlignedReadLengthMeanR1AlignedReadLengthMedianR1AlignedReadLengthModeR1AlignedReadLengthStandardDeviationR1DeletedBasesR1DeletedBasesPctR1InsertedBasesR1InsertedBasesPctR1MappedBasesR1MappedBasesPctR1MappedReadsR1MappedReadsPctR1MatchedBasesR1MatchedBasesPctR1MismatchedBasesR1MismatchedBasesPctR1PerfectBasesR1PerfectBasesPctR1PerfectReadsR1PerfectReadsPctR1Q20BasesR1Q20BasesPctR1SoftClippedBasesR1SoftClippedBasesPctR1SoftClippedReadsR1SoftClippedReadsPctR1UnmappedBasesR1UnmappedBasesPctR1UnmappedReadsR1UnmappedReadsPctR1UnpairedReadsR1UnpairedReadsPctR1YieldBasesR1YieldReadsR2AlignedBasesR2AlignedBasesPctR2AlignedReadLengthMeanR2AlignedReadLengthMedianR2AlignedReadLengthModeR2AlignedReadLengthStandardDeviationR2DeletedBasesR2DeletedBasesPctR2InsertedBasesR2InsertedBasesPctR2MappedBasesR2MappedBasesPctR2MappedReadsR2MappedReadsPctR2MatchedBasesR2MatchedBasesPctR2MismatchedBasesR2MismatchedBasesPctR2PerfectBasesR2PerfectBasesPctR2PerfectReadsR2PerfectReadsPctR2Q20BasesR2Q20BasesPctR2SoftClippedBasesR2SoftClippedBasesPctR2SoftClippedReadsR2SoftClippedReadsPctR2UnmappedBasesR2UnmappedBasesPctR2UnmappedReadsR2UnmappedReadsPctR2UnpairedReadsR2UnpairedReadsPctR2YieldBasesR2YieldReadsSoftClippedBasesSoftClippedBasesPctSoftClippedReadsSoftClippedReadsPctTotalPairsTotalRecordsTotalSameChrPairsTotalSameChrPairsPctUnfilteredRecordsUnfilteredRecordsPctUnmappedBasesUnmappedBasesPctUnmappedReadsUnmappedReadsPctUnpairedReadsUnpairedReadsPctWgsAlignedReadsWgsAlignedReadsPctWgsCalculatedAlignedReadsWgsCovDuplicateReadsWgsCovDuplicateReadsPctWgsCoverageBases1WgsCoverageBases10WgsCoverageBases100WgsCoverageBases1000WgsCoverageBases1000PctWgsCoverageBases100PctWgsCoverageBases10PctWgsCoverageBases15WgsCoverageBases15PctWgsCoverageBases1PctWgsCoverageBases20WgsCoverageBases20PctWgsCoverageBases30WgsCoverageBases30PctWgsCoverageBases40WgsCoverageBases40PctWgsCoverageBases50WgsCoverageBases500WgsCoverageBases500PctWgsCoverageBases50PctWgsCoverageBases60WgsCoverageBases60PctWgsCoverageBases70WgsCoverageBases70PctWgsCoverageMeanWgsCoverageMedianWgsCoverageStandardDeviationWgsExpectedAlignedReadsWgsFilteredLowBaseQualityBasesWgsFilteredOverlapBasesWgsReadsPairedWgsReadsPairedWithMatesWgsTotalReadsYieldBasesYieldReads
        RIH0_ANA0-HG002_DBC0_0.strobe
        strobe
        36,266,355.0
        98.0
        113.5
        148.0
        148.0
        63.9
        2.9
        27,658.0
        0.1
        6,216.0
        0.0
        42.0
        0.0
        0.0
        0.0
        results/day/hg38/RIH0_ANA0-HG002_DBC0_0/align/strobe/RIH0_ANA0-HG002_DBC0_0.strobe.mrkdup.sort.bam
        35,517,344.0
        562.0
        564.0
        546.0
        162.1
        28,975.0
        0.1
        36,640,212.0
        99.0
        316,088.0
        99.2
        36,041,748.0
        99.4
        195,632.0
        0.5
        26,539,771.0
        72.4
        180,161.0
        57.0
        35,081,664.0
        96.7
        18,238,683.0
        98.6
        114.0
        148.0
        148.0
        63.5
        13,591.0
        0.1
        14,229.0
        0.1
        18,372,720.0
        99.3
        158,484.0
        99.5
        18,146,620.0
        99.5
        77,834.0
        0.4
        13,636,641.0
        74.2
        92,452.0
        58.3
        17,922,578.0
        98.3
        134,037.0
        0.7
        4,033.0
        2.5
        127,132.0
        0.7
        859.0
        0.5
        991.0
        0.6
        18,499,852.0
        159,343.0
        18,027,672.0
        97.3
        112.9
        148.0
        148.0
        64.4
        14,067.0
        0.1
        14,746.0
        0.1
        18,267,492.0
        98.6
        157,604.0
        98.9
        17,895,128.0
        99.3
        117,798.0
        0.7
        12,903,130.0
        70.6
        87,709.0
        55.7
        17,159,086.0
        95.2
        239,820.0
        1.3
        6,490.0
        4.1
        257,372.0
        1.4
        1,739.0
        1.1
        111.0
        0.1
        18,524,864.0
        159,343.0
        373,857.0
        1.0
        10,523.0
        3.3
        157,493.0
        318,686.0
        153,254.0
        96.2
        318,686.0
        100.0
        384,504.0
        1.0
        2,598.0
        0.8
        748.0
        0.5
        247,569.0
        77.7
        247,527.0
        42.0
        0.0
        34,577,195.0
        22,133.0
        396.0
        0.0
        0.0
        0.0
        0.0
        11,106.0
        0.0
        1.1
        6,087.0
        0.0
        2,313.0
        0.0
        1,193.0
        0.0
        725.0
        0.0
        0.0
        0.0
        605.0
        0.0
        522.0
        0.0
        0.0
        0.0
        0.1
        247,569.0
        0.0
        0.0
        247,569.0
        246,780.0
        318,686.0
        37,024,716.0
        318,686.0

        Rih0 Ana0-Hg002 Dbc0 0.Strobe.Vb2

        Showing 0/1 rows and 18/18 columns.
        SEQ_IDRGCHIP_ID#SNPS#READSAVG_DPFREEMIXFREELK1FREELK0FREE_RHFREE_RACHIPMIXCHIPLK1CHIPLK0CHIP_RHCHIP_RADPREFRDPHETRDPALT
        RIH0_ANA0-HG002_DBC0_0.strobe
        NA
        NA
        NA
        NA
        NA
        0.0
        -1.0
        -1.0
        NA
        NA
        NA
        NA
        NA
        NA
        NA
        NA
        NA
        NA

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        Bcftools1.21
        FastQC0.11.9
        Samtools1.21